POMA AI: The Enterprise Data Intelligence Engine
Pitch Deck
Every company's competitive advantage lives in their knowledge—contracts, policies, customer data, domain expertise. Getting that knowledge into agentic AI context—properly and at scale—is the problem everyone talks about. Poor ingestion and chunking waste up to 99% of computational power while agents hallucinate. POMA AI solves this, enabling intelligent knowledge orchestration for agentic systems.
Funding Ask: €1.5 Mio
The RAG Paradox: Powering AI with Flawed Foundations
Massive Computational Waste and Stifled AI Scalability
Traditional Retrieval Augmented Generation (RAG) systems face a fundamental challenge: their inefficient document processing. This leads to a critical dilemma for enterprises:
Either they endure massive computational waste and severely degraded AI performance, or their AI applications remain confined to narrow, single-use cases, preventing widespread adoption and true enterprise-scale impact.
Much like how web search struggled with inefficiency before Google's PageRank algorithm, current context engines are severely hampered by inadequate data ingestion and sub-optimal chunking strategies.
By 2026, enterprises are projected to waste over $10 billion annually on inefficient document processing methods.
"Compute costs are more expensive for us than a lot of other things." - Winston Weinberg, Founder & CEO Harvey
The POMA AI Context Engine Solves This Paradox
Stage 1: Document Import and Conversion
Our intelligent document processing system imports >50 filetypes while preserving critical structural relationships.
Stage 2: Intelligent Chunking
Our patented technology divides documents into structurally meaningful chunks that maintain contextual integrity.
Stage 3: Embed, Store & Retrieve
Embedding, storing and retrieving chunks becomes automatically efficient for optimal context assembly.
Stage 4: Customer Integration
Seamless deployment into existing AI infrastructure for chats, agents and MCP ecosystems with minimal configuration.
Technology Deep Dive:
Revolutionary Document Chunking Technology
Our Context Engine is powered by our patented, industry-leading chunking technology.
Just like Google's PageRank, it is the core component that sets us apart:
Proprietary Chunking Algorithm
Our patented technology preserves in-document relationships and hierarchies by converting any document into trees and finding traversal paths through them. As a result, we maintain critical context that traditional chunking destroys.
Innovative Deduplication
Document- and query-specific optimization that significantly enhances retrieval accuracy while reducing computational requirements by up to 90%.
Universal Compatibility
We seamlessly integrate with all major LLM frameworks including OpenAI, Anthropic, Mistral and both proprietary or open-source models as well as LangChain and MCP connectors.
Our proprietary innovation creates a substantial competitive moat with patent granted at USPTO and utility patent in Germany. At the same time, chunking serves as a strong entry point for our go-to-market strategy, thanks to the limited competition in advanced chunking solutions.
POMA AI Business Model & Monetization Strategy
Segmented, Value-Based Pricing
We deploy a tiered, usage-based pricing tailored for distinct customer segments—from early-stage startups to large enterprise integrations.
Land-and-Expand Approach
Free entry tier fosters rapid adoption across startups and technical teams. As customer reliance grows, built-in upgrade paths drive account expansion and lifetime value.
Usage-Driven Revenue Growth
Core pricing scales with credit consumption and user needs, capturing expansion as our solutions become mission-critical and usage increases.
Straightforward Pricing Model:
Market Opportunity for POMA AI
1
Explosive Market Growth
POMA AI operates at the intersection of three fast-growing segments with $50B+ TAM expanding at 30-50% CAGR:
  • Intelligent Document Processing: $2.45B (2024) → $46.23B (2033), 35.2% CAGR*
  • Enterprise context & RAG: $1.85B (2025) → $67.42B (2034), 49.1% CAGR**
  • Vector Database Platforms: $3.04B (2025) → $7.13B (2029), 23.7% CAGR***
2
TAM-SAM-SOM Framework
Clear market penetration strategy:
  • TAM: All businesses, enterprises and OEMs wasting context tokens ≈ $10B (2026)
  • SAM: Verticals in Europe & North America requiring high accuracy, high efficiency and deal with sensitive data AI ≈ $3B
  • SOM: Initial wedge in developer community + innovative multinationals with €20-40M ARR potential within 36 months
3
Bottom-Up Market Analysis
Validates €15-30M ARR Target:
  • Revenue Path: Viable with 60 enterprise, 200 business and 1000 PAYG customers, targeting ~€15M ARR within 48 months (less than 0.5% EU market share needed).
  • Strong Unit Economics: LTV : CAC ratio of 5.5:1.
  • Growth Milestones: €1M (18 months), €5M (36 months), €15M (48 months).
Key demand catalysts include exploding IDP & enterprise context spend, compliance requirements, vector database boom, and infrastructure budget shifts. POMA AI's patented technology and GDPR-native architecture position it for growth from €30-50M initial market to $45B+ TAM within the decade.
Sources
POMA AI's Strategic Go-to-Market Flywheel: Unlocking Sustainable Advantage
To attack the broken and fragmented enterprise context market with its superior alternative, POMA AI leverages a powerful strategic flywheel to drive market entry, foster growth, and secure a defensible position. This continuous cycle, fueled by our unique technology and customer insights, ensures long-term competitive advantage against players like Reducto.ai, Unstructured.io, and LlamaParse.
Uncontested Market Entry
Our patent-protected chunking technology enables a unique entry point with minimal direct competition.
Deep Customer Insights
Processing customer data provides unparalleled visibility into their specific problems and pain points.
Strategic Upselling & Cross-selling
Data-driven insights unlock and maximize valuable upselling and cross-selling opportunities.
Integrated Value Chain
Deep integration into the customer's value chain builds a highly defensible market position.
Reinforced Competitive Moats
Strong patent protection solidifies our competitive advantages and creates significant barriers to entry.
Continuous System Evolution
Our platform continuously learns and improves from customer data, becoming smarter over time.
Detailed Competitor Landscape of Context Engine Solutions
Our Team
Core team has been working together for 10+ years and has 70+ years of coding experience.
Dr. Alexander Kihm
CEO & Founder
Ph.D. Big Data Econometrics at German Aerospace Center
Serial entrepreneur: Advo Assist, fairr (exit to Raisin)
25+ yrs coding experience
Jens Jennissen
CFO & VP Strategy
20+ yrs experience in finance, legal, startups
Serial entrepreneur: fairr (exit to Raisin), JJs Manöverschluck
Sales Team
Senior Sales (freelance): 10+ yrs experience, sales for Hubspot and MongoDB
Business Developer: 7+ yrs experience
Engineering Team
Gen AI Developer: 10+ yrs machine learning experience
Senior Developer: PhD, 10+ yrs coding experience
Senior Developer: 10+ yrs backend
Developer: 10+ yrs frontend
Developer: 5+ yrs coding experience, BSc thesis on AI
Key Positions Hiring Roadmap
  • Product Manager (Q2 2026) - ready to sign
  • AI / IDP Researcher (Q3 2026)
  • Account Executive (Q3 2026)
  • Customer Success (Q4 2026)
Financial & KPI Targets for End of 2026
PAYG Customers
Business Customers
Assumptions
Churn Rate/Customer Life Time: 5% / 20 months
PAYG Ingestion Pages per Month: 5,000
Storage Adoption: 40% of PAYG customers
PAYG Storage & Retrieval per Month: 2,000 pages & 2,000 retrievals
Business: flat revenue of €2,500/month, roughly equivalent to to 100,000 pages. Very conservatively assumes no growth in monthly revenue.
Risk Mitigation Strategy
Technical Risk
Challenge: Scaling technology to enterprise volumes
Mitigation:
  • Phased development approach with regular benchmarking
  • Early beta testing with key customers
  • Production-ready containerized deployment architecture compatible with all major clouds and LLMs
Market Risk
Challenge: Enterprise sales cycles and adoption barriers
Mitigation:
  • Discounted model for initial customers as ambassadors
  • Industry-specific case studies and ROI calculators
  • Strategic partnerships with established vendors
Execution Risk
Challenge: Competitor response and market positioning
Mitigation:
  • Aggressive patent protection strategy
  • Aggressive go-to-market strategy with multiple revenue streams for diversification
  • Flywheel Strategy for defensible position
Technological Risk
Challenge: Remaining indispensable as AI models evolve with larger context windows and data volumes.
Mitigation:
  • Our precise and efficient chunking intensifies demand as AI models grow.
  • Enables powerful AI models to effectively leverage vast information without dilution.
  • Ensures superior accuracy and relevance, solidifying our competitive advantage.
  • Positions POMA AI as an indispensable layer for future AI advancements.
Next Steps & Timeline
Q3 2025
✓ Closed First Check Round
✓ Launched beta program with German pilot customers
✓ Completed patent application process
✓ Hired Lead Sales, DevOps, Gen AI and BizDev
Q4 2025
✓ Launch conversion for PDF and 50+ other formats
Deliver full data intelligence engine
First revenue milestone
Q1 2026
Launch conversion for xls
Scale to €5k MRR
Launch financial services vertical
Hire Product Manager
Q2-4 2026
International expansion
Grow team and scale processes
Launch additional verticals
We have a clear roadmap to rapid scaling with defined KPIs and milestones to track progress and prepare for our Seed Round in 2027.
Ground-Floor Investment Opportunity at the AI Inflection Point
POMA AI is positioned to transform how companies leverage AI by eliminating the massive inefficiencies in current document processing systems.
With patented technology and a clear path to commercialization, we invite you to join us at this critical inflection point in AI adoption.
Contact: Dr. Alexander Kihm